Modern broadband wireless communication systems use multicarrier and multi antenna systems to increase system throughput. Single or multicarrier orthogonal frequency division multiple access (OFDMA) techniques have been adopted as various wireless communications standards, such as Long Term Evolution (LTE), Wi-Fi, and WiMAX, to name a few. Wideband Code Division Multiple Access (WCDMA), which has been adopted as the broadband standard for most existing wireless communication infrastructures today, also provides multiuser gains by using orthogonal codes for multiple simultaneous users in time domain. Standards employing OFDMA have advantages over traditional access technologies, such as TDMA and FDMA, in terms of scalability and multiuser gains.
Performing effective channel estimation in the presence of variable multiple access interference and multipaths, and subsequent equalization is important for data recovery at the receiver. Several channel estimation algorithms have been proposed that have the ability to estimate the channel in time/frequency domain adaptively. Recursive least square (RLS), least mean square (LMS), and Kalman filter (KF) based frequency domain channel estimation have been a few promising approaches in this respect. In RLS or LMS, channel estimation is carried out per time sample/frequency, usually based on a forgetting factor that adaptively controls the number of previous time sample/carrier contributions to the current channel estimate. In a Kalman filter based channel estimation process, a state space model of the channel is used to adaptively estimate and track the channel. These approaches have been shown to provide similar or significant performance improvement over the conventional block and static channel estimation algorithms, usually with the added advantage of having lower implementation complexities. Similarly, many low complexity linear equalization algorithms, including minimum mean square error (MMSE), zero forcing (ZF), and interference rejection combining (IRC), have been proposed for equalization of multiple access systems that require the estimated channel information to compute the equalization and spatial beamforming matrix.
In a wireless communication system, single or multiple data streams are multiplexed in time, frequency, or both, and space (antenna) and sent over the air using an efficient transmission scheme to increase system throughput, especially in a bandwidth limited scenario. In this process, the information bits in each data stream are separately channel coded, interleaved, and modulated to generate transmission symbol sequences, where each symbol sequence is taken from a pre-determined M-ary modulation set, where M represents the number of modulation symbols in the set. In addition to the data symbols, typically, additional known symbols are sent to facilitate coherent channel estimation at the receiver. The channel estimation can be performed per time sample/carrier, and/or jointly over all the data streams, or per a set of time sample/carriers and/or jointly over all the data streams. After channel estimation is performed, an equalization matrix is formed based on an MMSE, IRC or ZF criteria per stream or jointly over all the streams per time/frequency and is used to invert the effect of the channel on the received symbols. The equalized received symbols per data stream are then passed on to a demodulator to generate soft channel bits. These are then passed on to the channel de-interleaver and subsequently to the channel decoder to recover the information bits.
Using conventional methods, formation of the space-frequency equalization matrix requires matrix inversion, which introduces extra overhead in terms of complex multiplications and additions and numerical errors. This extra overhead can prohibit an efficient hardware implementation, especially in a rank deficient scenario that uses fewer observations than the number of received streams. This problem is typical for any multiple access wireless communication multiple access systems employing single or multiple streams.
Accordingly, there exists a need for methods, systems, and computer program products for adaptive channel estimation and equalization in a multicarrier communication system.